Word recognition method and word recognition program

ABSTRACT

A word recognition method of performing recognition processing with respect to each word candidate obtained by reading characters in character information written in a reading material is provided. This word recognition method includes a matching processing step of collating each word candidate with a plurality of words in a word dictionary and calculating, every word, a matching score indicative of a degree that each word candidate matches with a word, a character quality score calculating step of calculating a character quality score indicative of a degree that a character candidate constituting each word candidate matches with an arbitrary character, and a correcting step of correcting a matching score obtained at the matching processing step based on a character quality score acquired at the character quality score calculating step.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a Continuation Application of PCT Application No.PCT/JP2008/053433, filed Feb. 27, 2008, which was published under PCTArticle 21(2) in Japanese.

This application is based upon and claims the benefit of priority fromprior Japanese Patent Application No. 2007-065522, filed Mar. 14, 2007,the entire contents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a word recognition method ofrecognizing words and a word recognition program that is used to executeword recognition processing in, e.g., an optical character readingapparatus that optically reads a word including a plurality ofcharacters written in, e.g., a reading material.

2. Description of the Related Art

For example, in an optical character reading apparatus, when reading acharacter written in a reading material, accurate reading can begenerally performed by using knowledge of words even though an accuracyfor recognizing each character is low. Various kinds of methods havebeen conventionally proposed as implementation methods.

Among others, there is a method disclosed in Jpn. Pat. Appln. KOKAIPublication No. 2001-283157 as a method of using a posterioriprobability as an evaluation value for each word to enable accurate wordrecognition even if the number of characters is not fixed.

However, the method disclosed in the above-explained document isprovided on the assumption that a position where a word is written isalready known, and it cannot be said that word recognition can beperformed with a sufficient accuracy when the position where the word iswritten is unknown. For example, when a correct word is disorderlywritten or an incorrect word is fairly written, an evaluation value (amatching score) of a word in a dictionary similar to the incorrect wordrises, and erroneous recognition is thereby apt to be carried out.

BRIEF SUMMARY OF THE INVENTION

Therefore, it is an object of the present invention to provide a wordrecognition method and a word recognition program that can accuratelyperform word recognition even if a position where a word is written isunknown.

According to the present invention, there is provided a word recognitionmethod of performing recognition processing with respect to each wordcandidate obtained by reading characters in character informationwritten in a reading material, comprising: a matching processing step ofcollating each word candidate with a plurality of words in a worddictionary and calculating, every word, a matching score indicative of adegree that each word candidate matches with a word; a character qualityscore calculating step of calculating a character quality scoreindicative of a degree that a character candidate constituting each wordcandidate matches with an arbitrary character; and a correcting step ofcorrecting a matching score obtained at the matching processing stepbased on a character quality score acquired at the character qualityscore calculating step.

According to the present invention, there is provided a word recognitionprogram that allows a computer to perform recognition processing withrespect to a word candidate obtained by reading characters in characterinformation written in a reading material, comprising: a matchingprocessing step of collating each word candidate with a plurality ofwords in a word dictionary and calculating, every word, a matching scoreindicative of a degree that each word candidate matches with a word; acharacter quality score calculating step of calculating a characterquality score indicative of a degree that a character candidateconstituting each word candidate matches with an arbitrary character;and a correcting step of correcting a matching score obtained at thematching processing step based on a character quality score acquired atthe character quality score calculating step.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

FIG. 1 is a block diagram schematically showing a structure of a wordrecognition system that realizes a word recognition method according toan embodiment of the present invention;

FIG. 2 is a flowchart for explaining an outline of the word recognitionmethod;

FIG. 3 is a view showing an example of an image of, e.g., a form in analphabet-using countries fetched by, e.g., scanner;

FIG. 4 is a view showing an example of word candidates detected from afetched image;

FIG. 5 is a view showing an example of a word dictionary having cityname words registered therein;

FIG. 6 is a flowchart for explaining the word recognition methodincluding correction processing;

FIG. 7 is a view showing an example of matching scores stored in amatching score table;

FIG. 8 is a view showing an example of character quality scores storedin a character quality score table;

FIG. 9 is a view showing an example of correction cores stored in acorrected score table;

FIG. 10 is a flowchart for explaining details of word matchingprocessing in FIG. 6;

FIG. 11 is a flowchart for explaining details of word quality scorecalculation processing in FIG. 6; and

FIG. 12 is a flowchart for explaining details of corrected scorecalculation processing in FIG. 6.

DETAILED DESCRIPTION OF THE INVENTION

An embodiment according to the present invention will now be explainedhereinafter with reference to the accompanying drawings.

FIG. 1 schematically shows a structure of a word recognition system thatrealizes a word recognition method according to an embodiment of thepresent invention.

In FIG. 1, this word recognition system is formed of a CPU (a centralprocessing unit) 1, an input device 2, a scanner 3 as image inputtingmeans, a display device 4, a first memory 5 as storing means, a secondmemory 6 as storing means, a reading device 7, and others.

The CPU 1 executes an operating system program stored in the secondmemory 6 and an application program (e.g., a word recognition program)stored in the second memory 6 to perform, e.g., word recognitionprocessing that will be explained later in detail.

The input device 2 is formed of, e.g., a keyboard, a mouse, and others,and utilized by a user to perform various kinds or operations or inputvarious kinds of data.

The scanner 3 reads each character in a word written in a readingmaterial based on optical scanning, and inputs the read character.

The display device 4 is formed of, e.g., a display device or a printer,and outputs various kinds of data.

The first memory 5 is formed of, e.g., an RAM (a random access memory),and utilized as a working memory for the CPU 1 to temporarily storevarious kinds of data that are being processed. For example, the firstmemory 5 temporarily stores, a character dictionary 9, a word dictionary10, a probability table 11, and others which will be explained later.

The second memory 6 is formed of, e.g., a hard disk device, and storesvarious kinds of programs and others to operate the CPU 1. The secondmemory 6 stores an operating system program that is used to operate theinput device 2, the scanner 3, the display device 4, the first memory 5,the second memory 6, the reading device 7, and others, a wordrecognition program, the character dictionary 9 for recognition ofcharacters constituting each word, the word dictionary 10 for wordrecognition, the probability table 11 storing appearance probabilitiesof characters constituting each word, and others. The word dictionary 10stores a plurality of candidates of words that should be recognized inadvance, and it is a city name dictionary in which areas where the wordrecognition systems are installed, e.g., city names in states areregistered.

The reading device 7 is formed of, e.g., a CD-ROM drive device and readsthe word recognition program or the word dictionary 10 for wordrecognition stored (saved) in a CD-ROM 8 as a storage medium. The wordrecognition program, the character dictionary 9, the word dictionary 10,and the probability table 11 read by the reading device 7 are stored(saved) in the second memory 6.

An outline of the word recognition method will now be explainedhereinafter with reference to a flowchart in FIG. 2.

First, image fetching processing of fetching (reading) an image in apostal matter P by the scanner 3 is carried out (step ST1). Regiondetection processing of detecting a region where an address is writtenbased on the image fetched by this image fetching processing isperformed (step ST2). Cutout processing of using vertical projection orhorizontal projection to cut out a character pattern in a rectangularregion in accordance with each character in a word corresponding to acity name from the address written region detected by this regiondetection processing is performed (step ST3). Character recognitionprocessing of obtaining character recognition candidates based on adegree of similarity obtained by comparing the character pattern of eachcharacter in the word cut out by this cutout processing with a characterpattern stored in the character dictionary 9 is effected (step ST4).Word recognition processing of calculating posteriori probabilities ofrespective city names in the word dictionary 10 and recognizing a cityname having the highest posteriori probability as a word by using arecognition result of each word obtained by the character recognitionprocessing, each character in the city names stored in the worddictionary 10, and the probability table 11 is carried out (step ST5).Each processing is controlled by the CPU 1.

A description will now be given as to an example of reading a city namefrom an address written in, e.g., a form in alphabet-using countries asa specific example.

FIG. 3 is a view showing an example of an image of, e.g., a form inalphabet-using countries fetched by, e.g., a scanner. FIG. 4 is a viewshowing an example of word candidates detected from the image fetchedby, e.g., the scanner. FIG. 5 is a view showing an example of a worddictionary having city name words registered therein. In this case, suchword candidates as shown in the image in FIG. 4 are detected from theimage in FIG. 3, these word candidates are searched for words registeredin the word dictionary (e.g., city names registered in the worddictionary in FIG. 5), and words written in the form or the like (e.g.,city names) are specified. At this time, predetermined correctionprocessing is performed with respect to a result of matching processingfor the word candidates and the words in the word dictionary in thisembodiment in particular.

Here, the word recognition method including the correction processingaccording to this embodiment will now be explained with reference to aflowchart in FIG. 6.

When such an image of, e.g., a form as depicted in FIG. 3 is fetchedthrough, e.g., a scanner, each word candidate included in the image isdetected as shown in FIG. 4 (step 11). A serial number is given to eachdetected word candidate.

Word matching processing for each word candidate is carried out (step12). The word matching processing is processing of collating each wordcandidate with a plurality of words in the word dictionary to calculate,every word, a matching score indicative of a degree that the wordcandidate matches with a word in the dictionary.

A known technology called DP (Dynamic Programming) matching (or “dynamicplanning method”) can be applied to this word matching processing. ThisDP matching is well known as an algorithm that minimizes computationalprocessing of calculating, e.g., a degree of similarity when collatingobjects (e.g., words) having different element numbers (e.g., characternumbers), and it performs association between elements (alignment) whileconsidering, e.g., a deviance of the elements to realize optimumcollating processing. Availing this technology enables realizing, e.g.,an improvement in a speed of the matching processing.

Matching scores calculated by the word matching processing are stored ina matching score table. FIG. 7 is a view showing an example of matchingscores stored in the matching score table. This matching score tablestores matching scores, every word, as matching processing results ofrespective word candidates having serial numbers 1 to 7 and city namewords “STOCKHOLM”, “TOCHICA”, “MOHEDA”, . . . in the word dictionary.

“TOSHIBA” indicative of a town name is written in an actual form as theword candidate having the serial number 3, and it can be expected thatits matching score with respect to each city name word in the worddictionary becomes low. However, the word dictionary has a city nameword “TOCHICA” in which five characters, match with “TOSHIBA” and twocharacters are different from the same. Since each characterconstituting “TOSHIBA” is fairly written in the form, a matching score(reference character a) with respect to the city name word “TOCHICA” inthe word dictionary becomes high because of the matching fivecharacters.

On the other hand, “StockHolm” indicative of a city name is written inthe actual form as the word candidate having the serial number 6, and itcan be expected that its matching score with respect to a city name word“STOCKHOLM” in the word dictionary becomes high. However, “STOCKHOLM” isdisorderly written in the form, its matching score (reference numeral b)is not that high as expected.

At this time, the matching score denoted by reference numeral a ishigher than the matching score designated by reference numeral b, andhence “TOSHIBA” written in the form may be possibly erroneouslyrecognized as the city name word “TOCHICA”. This embodiment avoids thiserroneous recognition based on the following respective steps.

After the word matching processing, character quality score calculationprocessing for each word candidate is carried out (step 13). Thischaracter quality score calculation processing is processing ofcalculating a character quality score indicative of a degree that eachcharacter constituting each word candidate matches with an arbitrarycharacter (any character in alphabets in the character dictionary). Forexample, when calculating a quality score of a given character candidatein a given word candidate, a probability (or a degree of similarity)that this character candidate matches with any one of alphabets in thecharacter dictionary is calculated. A result of addition of individualquality scores acquired by performing such calculation processing withrespect to each character candidate is adopted as a character qualityscore of this word candidate.

In this character quality score calculation processing, theabove-explained DP matching can be likewise applied when performing thecharacter candidate matching processing.

The character quality scores calculated by the character quality scorecalculation processing are stored in a character quality score table.FIG. 8 is a view showing an example of character quality scores storedin the character quality score table. This character quality score tablestores character quality scores with respect to the word candidateshaving the serial numbers 1 to 7.

For example, in the word candidate having the serial number 3 or theword candidate having the serial number 7 written in the form, eachcharacter is fairly written. In particular, since a boundary betweencharacters is clear, each character can be specified without fail. Sincea shape of each character to be specified substantially matches with anarbitrary alphabet in the word dictionary, a character quality score ofsuch a candidate is higher than those of other candidates. For example,in case of the word candidate “TOSHIBA” having the serial number 3,hand-written characters “T”, “O”, “S”, “H”, “I”, “B”, and “A” are fairso that they substantially match with alphabets “T”, “O”, “S”, “H”, “I”,“B”, and “A” in the character dictionary (a degree that these charactersmatch with or similar to these alphabets is high), and hence a highscore is given. In case of the word candidate having the serial number7, a high score is likewise given.

On the other hand, in the word candidate having the serial number 4 orthe word candidate having the serial number 6 written in the form, eachcharacter is disorderly written in a running hand. In particular, sincea boundary between characters is unclear, specifying each character isdifficult, and each character is apt to be erroneously specified. Evenif each character can be correctly specified, it is often the case thata shape of each character does not substantially match with an arbitraryalphabet in the character dictionary. Therefore, such a word candidatehas a lower character quality score than those of the other candidates.For example, the disorderly written word candidate “StockHolm” havingthe serial number 6 corresponds to this case, and it has a low score.The word candidate having the serial number 4 likewise has a low score.

After the character quality score calculation processing, correctedscore calculation processing with respect to each word candidate isexecuted (step 14). The corrected score calculation processing isprocessing of correcting the matching scores obtained by the wordmatching processing based on the character quality scores acquired bythe character quality score calculation processing. For example,processing of subtracting a character quality score obtained by thecharacter quality score calculation processing from a matching scoreacquired by the word matching processing. As a result, a matching scoreof a word candidate having fairly written characters is greatly reducedby the correction processing and, on the other hand, a matching score ofa word candidate having disorderly written characters is slightlyreduced by the correction processing.

Corrected scores calculated by the corrected score calculationprocessing are stored in a corrected score table. FIG. 9 is a viewshowing an example of corrected scores stored in the corrected scoretable. This corrected score table stores corrected scores of respectivewords as a result of correcting matching scores which are matchingprocessing results of the respective word candidates having the serialnumbers 1 to 7 and the city name words “STOCKHOLM”, “TOCHICA”, “MOHEDA”,. . . in the word dictionary.

At a point in time where the word matching processing is executed, thematching score (reference character a) of the word candidate having theserial number 3 and the word “TOCHICA” in the dictionary is higher thanthe matching score (reference character b) of the word candidate havingthe serial number 6 and the word “STOCKHOLM” in the dictionary, butlevels of the scores are reversed after the corrected score calculationprocessing. That is, after the corrected score calculation processing,the matching score (the corrected score) (reference character b′) of theword candidate having the serial number 6 and the word “STOCKHOLM” inthe dictionary is higher than the matching score (the corrected score)(reference character a′) of the word candidate having the serial number3 and the word “TOCHICA” in the dictionary. As a result, obtaining acorrect recognition result can be expected.

After the corrected score calculation processing, a city name wordhaving the highest corrected score in the corrected score table isselected from the word dictionary, and the selected city name word isoutput as a recognition result (step 15).

Details of the word matching processing depicted in FIG. 6 will now beexplained with reference to a flowchart in FIG. 10.

First, 1 is set to a number i of word candidates (step 21). Then, an ithword candidate is selected (step 22).

Subsequently, 1 is set to a number j of words in the word dictionary(step 23). Further, a jth word in the word dictionary is selected (step24).

Then, matching processing with respect to the selected ith wordcandidate and jth word in the dictionary is executed to calculate amatching score (step 25). Furthermore, the matching score is written ata position (i, j) in the matching score table (step 26).

Here, j is compared with the number of all words in the dictionary (step27). If j is smaller than the number of all words in the dictionary, 1is added to j (step 28), and the processing from the step 24 isrepeated. On the other hand, if j is not smaller, i is compared with thenumber of all word candidates (step 29). If i is smaller than the numberof all word candidates, 1 is added to i (step 30), and the processingfrom the step 22 is repeated. On the other hand, if i is not smaller,the word matching processing is terminated.

Details of the character quality score calculation processing in FIG. 6will now be explained with reference to a flowchart in FIG. 11.

First, 1 is set to a number i of word candidates (step 41). Moreover, anith word candidate is selected (step 42).

Then, a character quality score of character candidates constituting theword candidate is calculated (step 43). Additionally, the characterquality score is written at a position i in the character quality scoretable (step 44).

Subsequently, i is compared with the number of all word candidates (step45). If i is smaller than the number of all word candidates, 1 is addedto i (step 46), and the processing from the step 42 is repeated. On theother hand, if i is not smaller, the character quality score calculationprocessing is terminated.

Details of the corrected score calculation processing in FIG. 6 will nowbe explained with reference to a flowchart in FIG. 12.

First, 1 is set to a number i of word candidates (step 51). Further, avalue at the position i in the character quality score table is read asa correction value h (step 52).

Then, 1 is set to a number j of words in the word dictionary (step 53).Furthermore, a value at the position (i, j) in the matching score tableis read as a score s (step 54). Moreover, the correction value h issubtracted from the score s, and a calculation result is written at aposition (i, j) in the corrected score table (step 55).

Subsequently, j is compared with the number of all words in thedictionary (step 56). If j is smaller than the number of all words inthe dictionary, 1 is added to j (step 57), and the processing from thestep 54 is repeated. On the other hand, if j is not smaller, i iscompared with the number of all word candidates (step 58). If i issmaller than the number of all word candidates, 1 is added to i (step59), and the processing from the step 52 is repeated. On the other hand,if i is not smaller, the corrected score calculation processing isterminated.

Then, specific examples of computational expressions that are used tocalculate the various kinds of scores will now be explained.

Here, a word in the word dictionary, a character recognition result ofall character candidates, a universal set of positions of a charactercandidate, and a universal set of paths from a left end to a right endof a given position are defined as follows, respectively.

Word in the dictionary: w_(i)=c_(i1), c_(i2), . . . ), c_(i)jεC (C is aset of alphabets)

Character recognition result of all character candidates: r=(r₁, r₂, . .. )

Universal set of positions: LεL

Universal set of paths from a left end to a right end of a position L:nεN

To calculate a matching score of a given word candidate and a given wordin the dictionary, a calculation using a posteriori probability ratio isperformed. In this case, as a basic computational expression, thefollowing Expression (1) is adopted, for example.

$\begin{matrix}{{\log\frac{P\left( w_{i} \middle| r \right)}{P\left( w_{i} \right)}} \cong {\sum\limits_{i}{\log\frac{P\left( r_{i} \middle| c_{ij} \right)}{P\left( r_{i} \right)}}}} & {{Expression}\mspace{14mu}(1)}\end{matrix}$

A numerator of a fractional part on a left-hand side in Expression 1corresponds to a posteriori probability, and a denominator of the samecorresponds to a priori probability.

To calculate a matching score (corresponding to the score table in FIG.7) in the word matching processing based on Expression (1), thefollowing Expression (2) is used.

$\begin{matrix}{{{match}\left( {L,w_{i}} \right)} \equiv {\max\limits_{n \in {??}}\left\lbrack {\prod\limits_{j}\;\left\{ {{P\left( r_{n_{j}} \middle| c_{ij} \right)}/{P\left( r_{n_{j}} \right)}} \right\}} \right\rbrack}} & {{Expression}\mspace{14mu}(2)}\end{matrix}$

Further, to calculate a character quality score (corresponding to thescore table in FIG. 8) in the character quality score calculationprocessing, the following Expression (3) is used.

$\begin{matrix}{{{match}\left( {L,C^{*}} \right)} \equiv {\max\limits_{n \in {??}}\left\lbrack {\prod\limits_{j}\;\left\{ {{P\left( r_{n_{j}} \middle| C \right)}/{P\left( r_{n_{j}} \right)}} \right\}} \right\rbrack}} & {{Expression}\mspace{14mu}(3)}\end{matrix}$where, “C*” in Expression (3) is indicative of an arbitrary alphabet.

At last, to calculate a maximum value of a corrected score(corresponding to the score table in FIG. 9) in the corrected scorecalculation processing, the following Expression (4) is used as anevaluation function.

$\begin{matrix}{{{value}\left( w_{i} \right)} \equiv {{P\left( w_{i} \right)}{\max\limits_{L \in \mathcal{L}}\left\{ {{{match}\left( {L,w_{i}} \right)}/{{match}\left( {L,C^{*}} \right)}} \right\}}}} & {{Expression}\mspace{14mu}(4)}\end{matrix}$

According to the foregoing embodiment, even if a position where a wordis written is unknown, word recognition can be accurately performed. Forexample, even if a correct word is disorderly written and an incorrectword is fairly written, a matching score is corrected, and anappropriate evaluation value can be obtained, thereby avoidingoccurrence of erroneous recognition.

It is to be noted that the present invention is not restricted to theforegoing embodiment as it is, and constituent elements can be modifiedand embodied on an embodying stage without departing from the scope ofthe invention. Furthermore, various inventions can be formed based onappropriate combinations of a plurality of constituent elementsdisclosed in the foregoing embodiment. For example, some constituentelements can be eliminated from all constituent elements disclosed inthe embodiment. Moreover, constituent elements in different embodimentscan be appropriately combined.

According to the present invention, when performing word recognition in,e.g., an optical character reading apparatus that optically reads a wordformed of a plurality of characters written in a reading material, wordrecognition can be accurately performed even if a position where theword is written is unknown.

1. A word recognition method of performing recognition processing withrespect to each word candidate obtained by reading characters incharacter information written in a reading material, comprising: amatching processing step of collating each word candidate with aplurality of words in a word dictionary and calculating, every word, amatching score indicative of a degree that each word candidate matcheswith a word; a character quality score calculating step of calculating acharacter quality score indicative of a degree that a charactercandidate constituting each word candidate matches with an arbitrarycharacter; and a correcting step of correcting a matching score obtainedat the matching processing step based on a character quality scoreacquired at the character quality score calculating step, wherein thecorrecting step includes processing by subtracting the character qualityscore obtained at the character quality score calculating step from thematching score acquired at the matching processing step.
 2. The methodaccording to claim 1, wherein the arbitrary character is one ofalphabetical letters.
 3. The method according to claim 1, furthercomprising a step of outputting a word having the highest matching scorein respective matching scores corrected at the correcting step as arecognition result.
 4. A non-transitory computer-readable medium storinga word recognition program that allows a computer to perform recognitionprocessing with respect to a word candidate obtained by readingcharacters in character information written in a reading material, theprogram comprising: a matching processing step of collating each wordcandidate with a plurality of words in a word dictionary andcalculating, every word, a matching score indicative of a degree thateach word candidate matches with a word; a character quality scorecalculating step of calculating a character quality score indicative ofa degree that a character candidate constituting each word candidatematches with an arbitrary character; and a correcting step of correctinga matching score obtained at the matching processing step based on acharacter quality score acquired at the character quality scorecalculating step, wherein the correcting step includes processing bysubtracting the character quality score obtained at the characterquality score calculating step from the matching score acquired at thematching processing step.